CN115201644A - XLPE cable insulation defect and moisture state diagnosis method, recording medium and system - Google Patents

XLPE cable insulation defect and moisture state diagnosis method, recording medium and system Download PDF

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CN115201644A
CN115201644A CN202210835277.0A CN202210835277A CN115201644A CN 115201644 A CN115201644 A CN 115201644A CN 202210835277 A CN202210835277 A CN 202210835277A CN 115201644 A CN115201644 A CN 115201644A
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moisture
insulation defect
xlpe
cable
insulation
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何若冰
陈煜兴
何平
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Yangjiang Power Supply Bureau of Guangdong Power Grid Co Ltd
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Yangjiang Power Supply Bureau of Guangdong Power Grid Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
    • G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
    • G01R31/1263—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation
    • G01R31/1272—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation of cable, line or wire insulation, e.g. using partial discharge measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent

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Abstract

The invention belongs to the technical field of cable defect diagnosis, and particularly relates to a method for diagnosing insulation defects and moisture states of XLPE cables. The invention also provides a non-transient readable recording medium storing the program of the method and a system containing the medium, and the program can be called by a processing circuit to execute the method.

Description

XLPE cable insulation defect and moisture state diagnosis method, recording medium and system
Technical Field
The invention belongs to the technical field of cable defect diagnosis, and discloses a method for diagnosing insulation defects and moisture states of XLPE cables, a recording medium and a system for storing programs capable of executing the method.
Background
Crosslinked polyethylene (XLPE) cables are widely used in the field of high voltage power transmission. Operational experience shows that power cables laid in rainy and humid areas often have reduced insulation performance due to moisture, and the potential hazards are accumulated and developed to a certain extent to rapidly cause cable failure. Due to the fact that installation and laying processes of the XLPE cable are complex, and the operation environment is severe, the XLPE cable is often affected with damp in different degrees at different positions of the cable, and the degree of partial discharge caused by the damp is different. The partial discharge phenomenon at the affected part contains the defect type information at the fault part, so the partial discharge measurement is considered as an effective means for detecting and identifying the affected degree of the XLPE cable.
However, the cable is affected with moisture or insulation defect does not necessarily cause partial discharge to some extent, and there are many ways to consider the moisture degree, and although the probability of causing insulation defect is high, the moisture degree is not necessarily positively correlated with insulation defect.
Disclosure of Invention
Aiming at the problems, the invention provides a method for diagnosing the insulation defect and the damp state of an XLPE cable, which is characterized in that modeling is carried out through a bidirectional neural network algorithm, the partial discharge state of the cable forms an array in a quantification mode in the modeling, and the array is added into a model as a variable parameter, so that the technical problems that the traditional cable defect detection method has larger error with the reality and is difficult to correct are solved.
The specific scheme comprises the following steps:
s1, measuring humidity parameter sets of fixed-length XLPE cables of the same type and with different humidity degrees and insulation defect types;
s2, measuring partial discharge parameter sets of XLPE cables with the same type and fixed length and different moisture degrees and insulation defect types;
s3, establishing fingerprint spectrums of the partial discharge parameters and the humidity parameters under different humidity degrees and insulation defect types;
s4, constructing a model by adopting a bidirectional circulation neural network according to the fingerprint;
and S5, substituting the local discharge value and the moisture parameter value measured on site into the model, identifying the insulation defect type of the cable and evaluating the moisture degree of the cable.
According to the technical scheme, quantified partial discharge factors are brought into a modeling category, the model is built through the bidirectional neural network, the model formed by multiple arrays can fit an originally uncertain complex correlation relationship, and the diagnosis accuracy is greatly improved.
Preferably, the partial discharge parameters include a discharge frequency and a discharge cumulative energy.
These two factors can simply generalize the extent of partial discharge.
Further, the moisture parameters include moisture content, dielectric loss, conductivity.
The three parameters of water content, dielectric loss and conductivity are the quantities used for measuring the moisture degree in the traditional method, and are compounded into the model, and the model is closer to reality by exerting compound influence on the establishment of the model together with the partial discharge parameters.
Furthermore, the insulation defect types are classified into 5 types, the step S5 further includes a step of measuring the diagnosis precision, the identification rate is adopted as a measurement standard for the identified insulation defect types, and a product calculation is introduced for the moisture degree to evaluate the error, so as to obtain the following objective function:
Figure BDA0003747667630000021
NFi is the number of defects of which the defect types in the sequence F are identified as Fi; NT is the length of sequence F;
Figure BDA0003747667630000022
converting the sequence into a phase number sequence of a five-bit two-input heating unique code; e is a unit matrix with 5 multiplied by 5 specification; k is the identification of the type of defectThe rate of differentiation; and p is the confidence probability of the judgment result.
The setting can reduce the calculation amount and the occupation of calculation resources on the premise of ensuring the precision requirement, and simultaneously, the model can be scientifically corrected by evaluating errors.
Another aspect of the present invention provides a non-transitory readable recording medium storing one or more programs comprising instructions which, when executed, cause a processing circuit to perform the method for diagnosing an XLPE cable insulation defect and a moisture condition as described above.
In another aspect, the present invention provides an XLPE cable insulation defect and moisture diagnosis system, which includes a processing circuit and a memory electrically coupled to the processing circuit, wherein the memory is configured to store at least one program, the program includes a plurality of instructions, and the processing circuit executes the program to perform the XLPE cable insulation defect and moisture diagnosis method.
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FIG. 1 is a schematic diagram of a bidirectional neural network algorithm of the present invention;
FIG. 2 is a schematic diagram of partial discharge state measurement and data acquisition according to an embodiment of the present invention;
wherein, 1, the pressure regulating unit; 2. a collecting unit; 3. a measuring unit; 4. an identification unit; 5. voltage regulating resistors; 6. a corona-free test transformer, 7, a protective resistor, 8, a capacitive voltage divider; 9. a coupling capacitor; 10. measuring the resistance; 11. an oscilloscope; 12. a synchronous control circuit.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention, and the described embodiments are some embodiments, but not all embodiments, of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any new work, are within the scope of the present invention.
As shown in fig. 1, the modeling in this embodiment adopts an algorithm structure of Bi-directional recurrent neural networks (BRNN). The BRNN algorithm is a novel neural network form obtained by expanding a one-way neural network algorithm, only focuses on forward data transmission, is different from the one-way neural network algorithm, the BRNN structure is formed by two neural networks in opposite directions, the BRNN algorithm allows internal circulation connection of the networks, and the repeated connection temporarily stores the previously input data in the networks, so that the output of the neural networks is influenced, the back propagation of information flow can be realized, and the influence of the previous input and the next input on the neural network algorithm is focused at the same time.
Prepare multiunit XLPE power cable of the same model fixed length, every group cable length is 2 meters, and the cable that the user accessible host computer switches the button and switches the cable sample that waits to detect, and the button starts the back, and the change over switch action is switched and is waited to detect the cable sample.
The moisture sensor is matched with an RFID receiver of an upper computer, so that parameter measurement of reflecting the moisture degree of the cable such as water content, dielectric loss and conductivity is realized, and a moisture parameter set is obtained; wherein the effective detection distance of the sheet sensor is more than or equal to 2.0m, and the temperature detection range is as follows: -40 ℃ to 100 ℃, water content detection range: 25 to 98 percent; and selecting the RFID receiver with the gain of 9dBi, the frequency range of 840/940MHz and the impedance of 50 omega so as to improve the detection efficiency.
As shown in fig. 2, the partial discharge measurement portion includes a voltage regulation unit 1, an acquisition unit 2, and a measurement unit 3; the voltage regulating unit 1 comprises a voltage regulating resistor 5, a corona-free test transformer 6, a protective resistor 7 and a capacitive voltage divider 8. The voltage regulating resistor 5 and the corona-free test transformer 6 generate alternating voltage, and the alternating voltage passes through the protective resistor 7 and the capacitive voltage divider 8 to provide voltage for the sample to be detected in the acquisition unit 2. The protective resistor 7 serves to limit the current through the corona-free test transformer 6 when the test sample is broken down, and to prevent it from being impacted. The capacitive voltage divider 8 is used for monitoring the voltage value at the output side of the corona-free test transformer 6 in real time and inputting the voltage value to the measuring unit 3 as a phase reference of the discharge signal.
The measuring unit 3 comprises a coupling capacitor 9, a measuring resistor 10, an oscilloscope 11 and a synchronous control circuit 12. The coupling capacitor 9 provides a path for the measuring resistor 10, the measuring resistor 10 is a pulse current experimental resistor, the pulse current is coupled to the measuring resistor 10 by a pulse current method, and partial discharge information is displayed through the oscilloscope 11. The oscilloscope 11 selects a Tektronix DPO 7000C oscilloscope with a FastFrame function, the measurement unit 3 can also be matched with various partial discharge measurement methods such as a high-frequency method, an ultrahigh-frequency method and an optical fiber method except for a pulse current method to perform partial discharge detection on the sample to be detected, and the obtained data sets such as discharge frequency, discharge accumulated energy and the like are transmitted to the identification unit 4.
The identification unit 4 establishes a fingerprint map by using the moisture degree detection and partial discharge measurement data set in the early database, wherein the fingerprint parameters comprise water content, dielectric loss, conductivity, discharge frequency and discharge accumulated energy, the insulation defect types are classified into 5 types from light to heavy, and the moisture degree adopts the empirical data in the early database. Constructing a model by adopting a bidirectional circulation neural network according to the fingerprint; and substituting the local discharge value and the moisture parameter value measured on site into the model, identifying the insulation defect type of the cable and evaluating the moisture degree of the cable.
And adopting the identification rate as a measuring standard for the identified insulation defect type, and introducing a product calculation to evaluate the error for the moisture degree, thereby obtaining the following objective function:
Figure BDA0003747667630000051
NFi is the number of defects of which the defect types in the sequence F are identified as Fi; NT is the length of sequence F;
Figure BDA0003747667630000052
converting into a stage number sequence of five-bit binary heating unique codes; e is a unit matrix with 5 multiplied by 5 specification; k is the recognition rate of defect types; and p is the confidence probability of the judgment result. And if the value of k or p cannot meet the requirement, adjusting the division of the insulation defect types or the detection type of the moisture data, and reconstructing the model to correct until the detection accuracy meets the requirement.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computers, usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The technical scheme of the invention is that the steps of the method are compiled into a program and then the program is stored in a hard disk or other non-transient storage media to form a non-transient readable recording medium; the storage medium is electrically connected with a computer processor, and the diagnosis of the insulation defect and the damp state of the XLPE cable can be completed through data processing, so that the technical scheme of the diagnosis system of the insulation defect and the damp state of the XLPE cable is formed.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (6)

1. A method for diagnosing insulation defects and moisture states of XLPE cables is characterized by comprising the following steps:
s1, measuring humidity parameter sets of fixed-length XLPE cables of the same type and with different humidity degrees and insulation defect types;
s2, measuring a partial discharge parameter set of XLPE cables with the same type and the same length and different damp degrees and insulation defect types;
s3, establishing fingerprint spectrums of the partial discharge parameters and the moisture parameters under different moisture degrees and insulation defect types;
s4, constructing a model by adopting a bidirectional circulation neural network according to the fingerprint;
and S5, substituting the local discharge value and the moisture parameter value measured on site into the model, identifying the insulation defect type of the cable and evaluating the moisture degree of the cable.
2. The method of claim 1, wherein the parameters of partial discharge include discharge frequency and accumulated energy of discharge.
3. The method for diagnosing insulation defects and moisture status of XLPE cable as claimed in claim 2, wherein said moisture parameters include moisture content, dielectric loss, and electrical conductivity.
4. A method for diagnosing insulation defect and moisture state of XLPE cable according to any of claims 1-3, characterized in that said insulation defect type is classified into 5 types, S5 further comprises a step of measuring the diagnosis accuracy, wherein the identification rate is used as the measure for said identified insulation defect type, and the error is evaluated by introducing the product calculation for said moisture degree, thereby obtaining the following objective function:
Figure FDA0003747667620000011
the NFi is the number of defects of which the defect types in the sequence F are identified as Fi; NT is the length of sequence F;
Figure FDA0003747667620000012
converting the sequence into a phase number sequence of a five-bit two-input heating unique code; e is a unit matrix with 5 multiplied by 5 specification; k is the recognition rate of defect types; and p is the confidence probability of the judgment result.
5. A non-transitory readable recording medium storing one or more programs comprising instructions which, when executed, cause a processing circuit to perform a method of XLPE cable insulation defect and moisture status diagnosis as claimed in claim 4.
6. An XLPE cable insulation defect and moisture diagnostic system comprising processing circuitry and a memory electrically coupled thereto, wherein said memory is configured to store at least one program, said program comprising a plurality of instructions, and wherein said processing circuitry executes said program to perform a method of XLPE cable insulation defect and moisture diagnostic as claimed in claim 4.
CN202210835277.0A 2022-07-15 2022-07-15 XLPE cable insulation defect and moisture state diagnosis method, recording medium and system Pending CN115201644A (en)

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